An AI glossary for business leaders

A cheat sheet on machine intelligence in the enterprise

As companies expand the use of artificial intelligence, so does the
vocabulary used to describe how AI applications and tools are changing
every business function across industries. Following is a glossary of
25 terms that cover the basic vocabulary of AI in the enterprise.

Adversarial machine learning A technique whereby developers introduce purposefully deceptive
data inputs to machine learning algorithms to trick them into making
incorrect conclusions or decisions. Adversarial data can be used as
part of advanced algorithm training; hackers can also use it wreak
havoc with AI applications.

AI council Some companies have created special groups to aid executives in
implementing AI initiatives. Farmers Insurance, for instance, runs two
AI
“councils.” One is focused on identifying business applications
and use cases for AI; the other council is IT‑focused and helps
identify the right enterprise tools, data sets, and other technical
requirements for building AI systems.

AI backstory An AI backstory is a character profile or persona that
businesses create as part of their development of chatbots, voice
assistants, and other AI applications. The backstories help determine
a myriad of chatbot behaviors, speech patterns, and personality traits.

AIOps AIOps refers to AI used in IT operations. IT Ops pros use AI to
analyze large amounts of operational data, then apply machine learning
to automate specific tasks such as performance monitoring or managing
service requests.

Algorithmic auditing A method for testing whether underlying algorithms in machine
learning applications are compromised by human or other biases.
Engineers can audit the code itself or feed it a variety of inputs and
look for problematic patterns in its decision‑making.

Black box “Black box” generally refers to complex algorithms used in AI
applications. They are typically proprietary algorithms and
inaccessible to outside review or management.

Bots Also called spiders, scrapers, or crawlers, bots are software
applications that automate a set of tasks. Search engines developed
some of the first bots to “crawl” websites for content and data that
helped improve the quality of search indexing and rankings.

Chatbots Also called virtual assistants or voice assistants, chatbots
are software programs that create conversational experiences with
people through text or voice via web browsers, mobile apps, messaging
applications and other means. They can answer questions and automate
tasks much more efficiently than people.

Conversational UI Also known as voice AI, conversational UI is software, powered
by natural language processing, that allows computers (and chatbots,
smart speakers, and other devices) to understand and interact with
human language.

Deep learning An advanced form of machine learning through which computers
train themselves to perform human‑like tasks, such as understanding
speech or making predictions. Instead of using predefined algorithms
to draw conclusions from data, deep learning uses multiple processing
layers (called neural networks) that allow computers to recognize
patterns and learn on their own.

Emotion recognition Some AI programs can be trained to identify human emotional
states, such as stress. Deep learning algorithms can be trained to
recognize a range of human emotions by studying patterns in a person’s
facial expressions and speech.

Machine bias Machine bias occurs when algorithms in AI systems draw
erroneous conclusions from data, either due to human error or intent,
or as a result of insufficient analysis.

Machine learning The ability of computers to learn from experience. Machine
learning algorithms are typically designed for specific tasks, such as
facial recognition or malware detection. The algorithms can be
“trained” on large volumes of data.

Machine vision Also known as computer vision, machine vision allows computers
to understand images and other visual content by capturing and
analyzing data via cameras or other means.

Neural network The foundational element required in deep learning. Modeled
loosely on the human brain, a neural network isn’t an algorithm, but
rather a multi‑layered framework that allows machine learning
algorithms to work in concert to analyze and learn from complex data sets.

Neuromorphic computing An emerging computer architecture modeled on the biological
network of the human brain, designed to overcome the limits of the
traditional Von Neumann architecture that defines most computing
today. Along with other next‑gen architectures like quantum computing,
neuromorphic architectures could power the demanding AI workloads of
the future.

Predictive analytics Tools that analyze vast quantities of data to predict an
outcome based on data patterns and other inputs. These analyses are
possible because computers are increasingly good at learning from
experience and understanding how certain conditions lead to specific outcomes.

Re‑skilling The process of training or teaching workers new skills to adapt
a company’s workforce for changing needs and technologies, especially
AI. With automation able to perform an increasing number of tasks that
were once done by humans, re‑skilling can help workers transition into
new jobs and roles.

“Right to explanation” As part of the European Union’s General Data Protection
Regulation (GDPR) that took effect in 2018, companies are required to
provide an explanation to customers whenever they are subject to
decisions made by machine learning applications, AI, or other advanced
technologies. The regulation also mandates aggressive privacy and
transparency standards for personal data.

Robotic process automation (RPA) Software applications designed to instruct other applications
to automate specific business processes such as clearing transactions,
processing data, or communicating with other computer systems.

Self‑managed systems Computer systems that require little if any human intervention
but are designed by humans to advance desired outcomes. These
automated systems may draw on elements such as machine learning to
improve their performance over time.

Security Automation and Orchestration (SAO) An advanced cybersecurity technique, increasingly used in
concert with machine‑learning and AI capabilities, that combines
automation of rote tasks with the ability to manage security processes
and workflows across disparate networks, locations, and platforms.

Shadow algorithms Algorithms used to test the fidelity of other algorithms.
Shadow algorithms can help detect and fix problems that arise when
data scientists unwittingly introduce biases and mistaken assumptions
into their algorithms.

Unstructured data Data that is not organized according to a specific framework or
defined variables that conventional computers can understand. Examples
include text, photos, and videos. The ability to process and work with
unstructured data, or to transform it into structured data, is a key
component of advanced AI systems.